The predictor \(H\)  is a multiple instance learning (MIL) model, as it receives as input a collection (i.e., a bag) of \(r\)  images (i.e., instances). MIL has a long tradition in machine learning [1]}, [2]}, [3]}, [4]} and in biomedical imaging in particular [5]}, [6]}, [7]}, [8]}. However, its applications to the task of ICH detection remain limited [9]}, [10]}, [11]}. Similarly to previous works [9]}, [10]}, [11]} we use the attention-based MIL framework recently developed in [15]}, which parametrizes such an MIL predictor \(H\)  by composing \(r\)  instance-wise encoders with an attention mechanism [16]}, [17]}, \(a\) , and a final classifier \(g\) . Succinctly, we can write \(H(X) = g(a( [f(x^{(1)}),f(x^{(2)}),\dots ,f(x^{(r)})]))\) . This MIL predictor, as well as the strongly supervised one, are depicted in fig:architectures.
